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International Journal of Business and Society, Vol. 20 No 1, 2019, 229-246
CONSUMERS’ PSYCHOGRAPHICS AND GREEN CONSUMPTION INTENTION: COMMUNITY SUPPORTED
AGRICULTURE BUSINESS MODEL IN CHINA
Wong Ming Wong Shantou University
Shian-Yang Tzeng Shantou University
ABSTRACT
This paper explores (a) who will be consumers, and (b) what factors will influence consumers’ intention in
view of the Community Supported Agriculture business model. Due to food safety issue in China, this paper
identifies consumers’ psychographic factors and their consumption behavior towards the CSA model. The
objective of the paper is to examine the (a) awareness of certified organic label, (b) food safety attitude, and
(c) green product awareness, and its related with customers’ purchase intention, as moderated by income and
gender, respectively. The research sample comprises 486 respondents from Shantou, Shenzhen, and
Guangzhou in China via a simple random sampling. The collected data were analyzed by the hierarchical
moderator regression analysis and multiple regression analysis in terms of moderating variables scale. The
result indicated that the (a) awareness of certified organic label, (b) green product awareness, (c) food safety
attitude, and (d) income have a positive direct influence on consumers’ purchase intention. The correlation
between food safety attitude and purchase intention was moderated by income, while gender did not moderate
the correlation between the (a) awareness of certified organic label, (b) green product awareness, (c) food
safety attitude, and (d) purchase intention.
Keyword: China; Community supported agriculture; Purchase intention; Organic food; Green consumption
1. INTRODUCTION
This paper studies consumers’ green consumptive behavior towards the Community Supported
Agriculture (CSA) business model in China’s market. In 2014, one of the famous fast food
suppliers, named as Shanghai Husi Food in China (Li, 2014), which is a unit of the OSI group from
the US (Li, 2014), provided expired beef and chicken products to McDonald’s, Papa John’s, Burger
King, KFC, and Pizza Hut in several cities in China, including to McDonald’s in Japan (Jourdan,
2014; Li, 2014). Specifically, after the Milk Scandal in 2008 (Ramzy & Yang, 2008), food safety
has become a topic of argument in China. Since 2008, China continuously experiences food safety
issues every year (The New York Time, 2018). Because of these, almost all Chinese consumers do
not solely look for reliable brands and distribution channels (Jourdan, 2014). They also emphasize
their demand for food safety by desiring organic vegetables and fruits (Hatton, 2015).
Corresponding author: Business School, Institute of Guandong and Taiwan, Shantou University, 243 Daxue Road, Shantou,
Guangdong China 515063, Email: [email protected]
230 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
The food safety issue has made consumers who become more concerned and supportive with local
farmers to ensure their food safety. At the same time, to earn a higher income and satisfy the market
demand, farmers have shifted their approaches towards planting by utilizing organic methods to
grow their foods (Hatton, 2015). Therefore, the CSA model has appeared in different countries.
CSA began in the early 1960s in Germany and Switzerland (FairShare CSA Coalition, 2016a;
JustFood, 2016a; Biodynamic Association, 2016). Japan started the CSA in the 1970s, which they
named “Teikei”, whereas in the US the CSA business model began in the 1980s (FairShare CSA
Coalition, 2016a; JustFood, 2016a; Biodynamic Association, 2016). Meanwhile, China started the
CSA model in 2008 (Hitchman, 2015).
Many studies have concluded that whatever the factors, either from individual or situational
elements, they have influenced consumers green behavior (Joshi & Rahman, 2015), such as income
(Wong & Mo, 2013), environmental awareness (Papadopoulos et al., 2014). Also, many studies
have adopted the Theory of Reasoned Action (Ajzen & Fishbein, 1980), the Theory of Planned
Behavior (Ajzen, 1991), Attitude-Behavior-Context model (Guagnano et al., 1995), Motivation-
Ability-Opportunity model (Olander & ThØgersen, 1985), Reciprocal Deterministic theory
(Phippls et al., 2013). Even if various theories have explained that attitude of consumers does not
affect their behavior, they only illustrate the correlation between attitude and behavior. Whereas,
there is a lock of the CSA model view to exploring the attitude and behavior of consumers in regard
to purchasing organic food. As a result, the CSA model was compared in terms of research of
reused product and remanufactured products, in which it should have different factors that
influence the consumers’ behavior. Furthermore, the paper was utilized by the Wheel of Consumer
Analysis (Peter & Olson, 2010) to design its research theoretical framework.
Due to the characteristics of the CSA model, the subject of “How” and “Why” should be interesting
to use to explore the consumers’ behavior in China’s market. For example, from the perspective of
the CSA model, the question is “How does the consumer income really affect their behavior to
purchase green products? “The second question would be “Does the gender make a difference in
consumers’ purchasing behavior?” Specifically, when their behavior is related to buy organic food
and to pay in advance. These two factors were raised because of the price and person in charge of
organic vegetable purchasing. From the market segmentation perspective, there are also two
questions in regard to the CSA model. The first question is “Who will be the consumers?”. The
second is “Why do they purchase?". Therefore, to combine four questions into two research
questions are (a) “Who will buy organic foods and pay in advance?” and (b) “What factors will
affect them to do so?”
2. LITERATURE REVIEW
This study reviews on green consumption comprising of green product, price and quality of green
products, green consumers’ intention and behavior, demographic factors, environmental
knowledge, trust, value, attitude, and consumers perception (Fisher et al., 2012; Narine et al., 2015;
Purohit, 2012; Wong & Mo, 2013; Wee et al., 2014; Wong & Zeng, 2015). For instance, Joshi and
Rahman (2015) reviewed 53 articles from 2000 to 2014 based on the Scopus database. They have
analyzed and summarized these factors which have influenced green purchase behavior. The
definition of the purchase behavior included purchase intention, willingness to pay, and
Wong Ming Wong, Shian-Yang Tzeng 231
consumption. Furthermore, these influential factors were various, such as individual factors like
attitude, value, emotion, knowledge, trust, and situational factors like price, product, quality, and
eco-labeling.
To comprehend the relation between factors of green consumers’ psychographics and their
consumption intention, this paper adopted the Wheel of Consumer Analysis (Peter & Olson, 2010).
Peter and Olson (2010) illustrated that consumer behavior involved people’s thought and feeling,
experience and actions when they perform consumption processes. Peter and Olson adopted four
components that consisted of (a) affect and cognition of consumer, (b) consumer environment, (c)
consumer behavior, and (d) marketing strategy, to illustrate how these components interact with
each other. Based on the Wheel of Consumer Analysis, the research theoretical framework is
presented in Figure 1. The paper consisted of two models because of the moderating variables. The
moderating variable of Model One is income that is measured by the ordinal scale. Gender, which
is measured by the nominal scale, is also the moderating variable for Model Two.
Figure 1: Theoretical Framework Design
This paper has two objectives. First, it examines the (a) awareness of certified organic label, (b)
food safety attitude, and (c) green product awareness of consumers about how they affect their (d)
purchase intention. Second, since the paper is related to organic food purchasing intention, income
and gender of consumers were added to explore green consumer studies. It accordingly examines
that these corrections were moderated by their income and gender, respectively. The paper
contributes on how to identify psychographic factors from the attitude, cognitive, and perception
of consumers towards the purchasing intention of green products that can be integrated into green
marketing strategies.
2.1. CSA Model
The characteristics of the CSA are to (a) unitize a member system, (b) pay in advance, (c) share
the risk with farmers, (d) offer food safety, and (e) protect the living environment (Just Food,
2016a; FairShare CSA Coalition, 2016b; JustFood, 2016b; Local Harvest, 2016). Conversely, these
local farmers produce fresh vegetables, fruits, eggs, meat, and flowers. They offer a variety of
232 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
payment plans with weekly home deliveries (normally two to three times per week) or at a
convenient drop-off location; and they produce fresh food according to the seasons (Just Food,
2016; Local Harvest, 2016; FairShare CSA Coalition, 2016b). These fresh foods are basically
organic food (Just Food, 2016) for the reason that farmers cannot utilize herbicides, pesticides, or
artificial fertilizers, where groundwater pollution and toxic residues on food are avoided
(Biodynamic Association, 2016). In terms of the definition of the green product (Dahlstrom, 2011),
the CSA model belongs to one of the green products and is a way to produce organic foods as one
of the business models.
2.2. Purchase Intention
Peter and Olson (2010) defined purchase intention as the consumer’s willingness to purchase a
product or/and service in the future. Conversely, purchase behavior indicates consumers’ action to
purchase a product and/or service in the past. The main difference between consumers’ purchase
intention and behavior is the timeframe.
With regards to the purchase intention, the researches found that the demographic factors affect
the purchase intention of consumers in terms of green product attributes. For instance, Wong and
Zeng (2015) explored the conflict between price and quality of remanufactured products that were
related to the relationship between consumer’s purchase intention and behavior, which was
moderated by income in China. Results showed that the price of remanufactured products affected
purchase intention, which was translated into consumers’ purchase behavior of remanufactured
products. In contrast to price, quality of manufactured products did not affect the consumers’
purchase intention and behavior. The income moderated the effect of purchase intention and
purchase behavior.
One similar research of Wee et al. (2014) examined the consumers’ perceptions, purchase
intentions, and purchase behavior relative to organic foods products in Malaysia. As a result, the
consumers’ purchase intention towards organic food significantly influenced the perception of
safety, health, environmental factors, and animal welfare of the products. Similarly, Narine et al.
(2015) indicated that living location, educational background, and income of consumers influenced
their willingness to pay for organic foods and vegetables in Trinidad. Because of that, the purchase
intention of consumers was adopted in this paper as the dependent variable to explore the
correlation with organic food.
2.3. Awareness of Certified Organic Label
The label is not just an attached tag; it also provides several functions such as identification,
branding elements (Kotler & Keller, 201 6) and communicates with consumers to deliver useful
information to the government’s regulation requirements (Solomon et al., 2015). There are
numerous names to represent green labels, such as eco-label, green logo, organic label, or energy
label (Borin, et al., 2011; Purohit, 2012). These certified labels or logos are an accredited process
by third-party associations in different countries and contain the meaning of “reassurance” for its
green products (Castka & Corbett, 2016). Furthermore, applying organic accreditation is voluntary
(Castka & Corbett, 2016).
Wong Ming Wong, Shian-Yang Tzeng 233
The functions of the certified organic label provide clear information about environmental
protection (Khare et al., 2013) and the safety of foods (Quah & Tan, 2010). The features of organic
food consisted of non-chemical residues in food production and environmental protection
(Biodynamic Association, 2016; Quah, & Tan, 2010). When an organic label provides clear and
positive environmental messages, it creates consumers’ interest (Khare et al., 2013) and an
evaluation of the green products (Borin et al., 2011). Therefore, the organic label becomes an
important element for consumers to identify organic food process and production (Purohit, 2012).
Its contents will affect the consumer’s value, attitude, and perception to purchase (Purohit, 2012).
Purohit (2012) illustrated that organic label had influenced consumers’ decision-making to
purchase green products. The messages and contents of the label had affected the consumers’
decision-making in purchasing and perceiving consumer effectiveness (Cucchiara et al., 2015)
because of consumers’ live healthy (Chauhan, 2011; Rahbar & Wahid, 2011; Speer, 2011).
Certified labels, or eco-labels, or eco-brands have a positive green brand image (Cucchiara et al.,
2015). Furthermore, the organic label will reveal its products processing. Because of these, the
paper will utilize the organic label as an independent variable.
H1: The awareness of the certified organic label of consumers has a correlation with their purchase
intention.
2.4. Food Safety Attitude
Food safety becomes a significant factor in the choice of food products (Langiano et al., 2012;
Yeung & Yee, 2012). Mainly, consumers desire to purchase organic food due to healthy life and
food safety issues (Quah & Tan, 2010). This group of consumers has an abundance of knowledge
about food safety, risk perception of foodborne diseases, environmental protection (Langiano et
al., 2012). The research by Langiano et al. (2012) in Cassino, Italy, indicated that when consumers
were living with children, the elderly, or pregnant women, they tend to be more aware foodborne
diseases and pathogens to have a healthy life. Furthermore, the food purchasing buyers who live
with mothers (51.4%), parents (29.5%), children (5.9%), and fathers (4. 2%), mostly purchase their
food from the supermarkets and/or shopping centers (63.5%). Specifically, married couples and
housewives are more concerned with food safety. Langiano et al. (2012) concluded the food safety
related to buyers’ purchase behavior. Regarding food safety topic in China, the research of Feng,
Feng, Tian, and Mu (2012) have explored the correlation between price, quality, and safety of
grape products in China. They found consumers were willing to pay for the safety of grape
products. In their studies, they concluded that consumers will pay a premium price for a “safety”
grape. Because of that, this paper utilized the food safety as an independent variable.
H2: The food safety attitude of consumers has a correlation with their purchase intention.
2.5. Green Product Awareness
There are two definitions of green product. First, green products and/or services have less impact
on environmental hazards (Chauhan, 2011; Speer, 2011). Second, green products are harmless to
human health (Chauhan, 2011; Speer, 2011). Green products are known as ecological products,
organic foods, or environmental friendly products that include renewed or recycled product
materials, by using minimal packaging; the products are harmless and non-polluting to the
234 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
environment, and where the products are originally grown without toxic chemical compounds
(Chauhan, 2011; Dahlstrom, 2011; Speer, 201; Ottman, 2004).
Ottman (2004) illustrated that in general, consumers are unable to identify the difference between
green and non-green products because they do not have knowledge of environmental protection. If
consumers are unaware of green products, they do not purchase. Additionally, they do not perceive
the benefits of purchasing green products. The existing problem is that consumers do not realize
the knowledge about environmental protection and the meaning of green products (Ottman, 2004).
Understanding the green product, environmental knowledge, and concerns will assist consumers
in their environmental attribute in their purchasing behavior. In developing awareness of a green
product, organizations attempted to augment consumers’ knowledge of the product and its
environmental attributes to identify the consumers’ purchasing behavior. For instance,
Papadopoulos et al. (2014) discovered that environmental awareness has a major influence on
consumers’ decision to purchase furniture, where consumers are willing to pay an extra 2 to 16 %
for eco-labeled furniture.
In addition, Yaacob and Zakaria (2011) illustrated a positive attitude towards green products,
particularly in developing countries like Malaysia. A research by Yaacob and Zakaria (2011)
showed that the green product awareness becomes an important element for consumers buying
powers on green products (Laheri & Anupam, 2015; Khare et al., 2013; Ottman, 2004). Because
of that, this paper has utilized the green product as an independent variable.
H3: The green product awareness of consumers has a correlation with their purchase intention.
2.6. Demographics: Income and Gender
Market segmentation is an important aspect of consumers’ behavior (Solomon, 2015) and targets
consumers who have similar needs and desires (Kotler & Keller, 2016). To segment target markets
(Armstrong, Kotler & Opresnik, 2017), consumers have been segmented by their demographic
factors and psychographic elements (Kotler & Keller, 2016; Solomon, 2015). Besides, the
demographic factors affect consumers’ purchase intention and behavior in terms of the product
attributes and living environment (Pride & Ferrell, 2016; Wong & Ho, 2013). Specifically, income
affects consumers’ lifestyle which is reflected in what they can afford to purchase (Pride & Ferrell,
2016). This is due to consumers allocating their income in terms of their lifestyle and age group
(Kotler & Keller, 2016).
With regards to the demographics impacts on green products, Wong and Mo (2013) adopted age,
gender, income, and a reference group of consumers to explore their purchase intention and
behavior in China. The results concluded that the income and reference group of consumers
affected their purchase intention, which turns into purchase behavior, for a remanufactured product
(Wong & Mo, 2013). However, Fisher et al. (2012) adopted demographic factors that consisted of
(a) gender, (b) age, (c) education, (d) marital status, (e) children, (f) income, and (g) race to explore
consumers’ daily behavior in the US. The daily behavior indicates consumers who are (a) buying
the green product, (b) using the recyclable product, (c) recycling, and (d) switching behavior from
non-green products to green products. Their results found that the income of an individual has a
Wong Ming Wong, Shian-Yang Tzeng 235
significant influence on green product buying behavior and products recycling. The gender of
consumers did not only affect the green products buying behavior and products recycling but also
impacted the consumers’ switching behavior from non-green products to green products. Because
of different finding results between Wong and Mo (2013) and Fisher et al. (2012), this paper has
adopted income and gender as the moderating variables to explore a correlation with their purchase
intention of green products.
H4: Income moderates the correlation between the organic label and purchase intention.
H5: Income moderates the correlation between the food safety and purchase intention
H6: Income moderates the correlation between the green product and purchase intention.
H7: Gender moderates the correlation between the organic label and purchase intention.
H8: Gender moderates the correlation between the food safety and purchase intention.
H9: Gender moderates the correlation between the green product and purchase intention.
3. Methodology
3.1. Research Sampling
This research utilized questionnaires to collect data in Shantou, Shenzhen, and Guangzhou, China
because of two reasons. First, food safety in China is a global issue (Ching & Tin, 2017).
Specifically, it mainly relates to agriculture products in China, such as mislabeling packaging,
abnormal coloring, and odors (Hunt, 2015). Second, the CSA model is limited by a few cities in
China. Because of the GDP, polities, and population in China, China divides its cities into five tires
(Smith, 2017). Based on the definition of tire cities, their residents’ average income levels are
various. Shenzhen and Guangzhou are owned by first-tier cities. Shantou is part of third-tier cities.
3.2. Data Collection
The data collection period took place over eight days at each city, covering four weekends at
shopping malls and community’s residences via a simple random sampling. The questionnaire was
a self-reported survey instrument and was written in Mandarin. The translated instrument (English
version) is provided in Table AT1. In China, Mandarin is one common language to communicate
for reading and writing. The participants completed the questionnaire within 10 to 15 min.
Furthermore, these participants were invited to participate in a face-to-face interview.
3.3. Measurement
The questionnaire was divided into two sections. The first section consisted of the organic label,
food safety, green product, and purchase intention. There were five questions to cover each topic
of the organic label, green product, and purchase intention. Three questions covered the topic of
food safety. In order to measure the respondent’s attitude and opinion, a 5-point Likert was utilized
with 1 indicating “strongly disagree” and 5 indicating “strongly agree”. The construct and scale
item of the questionnaire are presented in Table AT1. The second section of the questionnaire
236 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
consisted of location, gender, age, educational level, marital status, and monthly income. A pilot
study was conducted to assess the validity and reliability of the survey instrument.
4. Data Analysis and Interpretation
The research sample was composed of 486 respondents which comprised of 82 respondents from
the pilot test, 255 respondents from Shantou and 231 respondents from Shenzhen and Guangzhou.
Table AT2 at appendix lists the participants’ demographics. Table AT3 displays the coefficient
correlation among organic label, food safety, green product, purchase intention, age, educational
level, income.
The result of the validity indicated the score of the convergence validity (AVE) and discriminant
validity, and of the reliability indicated the score of the Coefficient Alpha (α) in Table 1. As shown
in Table 1, All values of the Coefficient α values were over 0.70, all four constructs were
satisfactory reliability (Hair, Black, Babin, Anderson & Tatham, 2006). These values of AVE are
over 0.50 (Fornell & Larcker, 1981). These values of discriminant validity are presented at Table
1, the diagonal elements in the matrix are the square roots of the AVE, the square roots of the AVE
are higher than the values of its corresponding rows and columns (Fornell & Larcker, 1981).
Therefore, convergence validity and discriminant validity appeared satisfactory validity for all
constructs (Hair et al., 2006; Fornell & Larcker, 1981).
Table 1: The Mean, Standard Deviation, Cronbach’s Alpha, Convergence Validity, and
Discriminant Validity for the measurement model.
Construct Number
of Items Mean Std. Dev.
Cronbach’s
Alpha (α)
Convergence
Validity
AVE
Discriminant Validity
OL FS GP PI
OL 5 3.56 .88 .89 .70 .84
FS 3 4.19 .75 .80 .71 .34 .85
GP 5 3.37 .84 .76 .51 .42 .34 .71
PI 5 3.80 .85 .90 .72 .49 .40 .37 .85
Note: The items on the diagonal represent the square roots of the Average of Variance Extracted (AVE); off-diagonal
elements are the Pearson correlation estimates.
OL: Organic Label, FS: Food Safety, GP: Green Product, PI: Purchase Intention.
4.1. Model One: Hierarchical Moderator Regression Analysis
As the moderator variable, dependent variable, and three independent variables were continuous
variables, Model One utilized hierarchical moderator regression analysis (Baron & Kenny, 1986).
The variables of the (a) organic label, (b) food safety, (c) green product, and (d) purchase intention
formed a summation index at the initial step. The technique of least squares was used with the main
independent variables, entering as a block in the first step. Thereafter, the moderating variables are
indicated in the second step. The following regression equation was analyzed as indicated below.
Wong Ming Wong, Shian-Yang Tzeng 237
ϒ1 = α+β1X1+ β2X2+β3X3+β4X4+β5X4X1+β6X4X2+β7X4X3+ε
Where, ϒ1 = purchase intention, α = intercept, X1= organic label, X2= food safety, X3 = green
product, X4 = income, and ε = random disturbance terms (where indicated a normal distribution
random residual whose mean is 0). The moderating variable of the income effect on the correlation
between the organic label and purchase intention was X4X1. The X4X2 presented the moderating
variable of income effects on the correlation between the food safety and purchase intention. The
X4X3 presented the moderating variable of income effects on the correlation between the green
product and purchase intention.
As an observation, Table 2 presents the results of the hierarchical moderator regression analysis.
First, the (a) income, (b) organic label, (c) food safety, and (d) green product have a significant
influence on the consumers’ purchase intention. However, the adjusted R2 value of 0.321 in Step 1
indicated that 32.1% of variations of the purchase intention was explained by the income, organic
label, food safety, and green product.
Second, after the moderating variable was added, Step 2 in Table 2 indicates that the correlation
between food safety and purchase intention was affected by income. Conversely, income did not
moderate the correlation either (a) between the organic label and purchase intention; or (b) between
the green product and purchase intention.
Moreover, the R2 from 0.327 increased to 0.333 and the adjusted R2 value was 0.324. It indicated
that 32.4 % of the variations on purchase intention were explained by the (a) income, (b) organic
label, (c) green product, (d) food safety, and (e) interaction effect between income and food safety.
In addition, the figures of “Tolerance” and “VIF” displayed that it did not have a multicollinearity
issue. As a result, Model One failed to reject the H1, H2, H3, H5 and failed to accept the H4 and H6.
Table 2: Hierarchical Regression Result of the Organic Label, Food Safety, Green Product,
Purchase Intention, and Income
B SE b β Tolerance VIF
Step 1
Constant .000 .037
Income .095 .038 .097* .982 1.020
Organic Label .345 .042 .345*** .783 1.277
Food Safety .228 .041 .228*** .829 1.206
Green Product .140 .042 .140** .786 1.272
R2 = .327, ΔR2= .321, F=58.304***
Step 2
Constant -.006 .038
Income .097 .038 .092* .978 1.022
Organic Label .343 .042 .343*** .780 1.281
Food Safety .234 .041 .234*** .823 1.214
Green Product .135 .042 .135** .782 1.277
IncomexOL -.056 .046 -0055 .686 1.458
238 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
IncomexFS .085 .041 .090* .754 1.326
IncomexGP -.025 .045 -.025 .700 1.429
R2 = .333, ΔR2= .324, F=34.155***
Note: Dependent Variable: Purchase Intention. *P< .05, **P< .01, ***P< .001.
4.2. Model Two: Multiple Regression Analysis
The research framework of Model Two consisted of (a) moderating variable, the gender that is a
dichotomy, (b) independent variables are the organic label, food safety, and green product as a
continuous variable, and (c) the dependent variable is the purchase intention as a continuous
variable. As Baron and Kenny (1986) illustrated, this research framework utilized multiple
regression analysis to test the moderating variable effect on the correlation between independent
variables and dependent variable by individual tested, because of the dichotomous variable. The
following regression equation of male was analyzed as indicated below.
ϒ1 = α+β1X1+ β2X2+β3X3+β4X4+β5X4X1+β6X4X2+β7X4X3+ε
Where, ϒ1 = purchase intention, α = intercept, X1= organic label, X2= food safety, X3 = green
product, X4 = male, and ε = random disturbance terms (where indicated a normal distribution
random residual whose mean is 0). The moderating variable of the male effects on the correlation
between the organic label and purchase intention wasX4X1. The X4X2 presented the moderating
variable of male effects on the correlation between the food safety and purchase intention. The
X4X3 presented the moderating variable of male effects on the correlation between the green
product and purchase intention. Regarding females, only X4 equals females in the above regression
equation.
Then, to measure the effect of a moderating variable, it examined the differences between the two
regressions coefficient derived from two separate samples. Baron and Kenny (1986) indicated that
the equation was given by Cohen and Cohen (1983). If the test statistic, z is greater than 1.96 or p-
value less than 0.05, it means that the null hypothesis will be rejected. It can conclude that the
predictor is likely to be a meaningful addition to the model because changes in the predictor’s value
are related to changes in the response variable. The equation of the differences between the two
regression coefficients is as follows, where b1 = unstandardized coefficient of male, b2 =
unstandardized coefficient of the female.
𝑧 =(𝑏1 − 𝑏2)
√𝑠𝑒𝑏1 2 + 𝑠𝑒𝑏2
2
As an observation, Table 3 presents the results of the two multiple regression analyses. First, the
purchase intention of male and female consumers was affected by the (a) organic label, (b) food
safety, and (c) green product. Second, the R2 of male consumers was 0.317, and the adjusted R2
value was 0.307. It indicated that 30.7% of the variations on purchase intention of male consumers
were explained by the (a) organic label, (b) food safety, and (c) green product. Also, the figures of
the “Tolerance” and “VIF” displayed that it did not have a multicollinearity issue. Third, the R2 of
Wong Ming Wong, Shian-Yang Tzeng 239
female consumers was 0.323, and the adjusted R2 value was 0.316. It indicated that 31.6% of the
variations on purchase intention of female consumers were explained by the (a) organic label, (b)
green product, and (c) food safety. Also, the figures of the “Tolerance” and “VIF” also displayed
that it did not have a multicollinearity issue.
Table 3: Multiple Regression Results of the Organic Label, Food Safety, Green Product, and
Purchase Intention
B SE b β Tolerance VIF
Male
Constant .028 .061
Organic Label .324 .072 .318*** .690 1.450
Food Safety .186 .068 .180** .783 1.274
Green Product .191 .067 .202* .674 1.483
R2 = .317 ΔR2= .0307 F=30.781***
Female
Constant -.015 .048
Organic Label .353 .053 .358*** .840 1.191
Food Safety .270 .051 .276*** .876 1.142
Green Product .123 .056 .117* .855 1.170
R2 = .323 ΔR2= .316 F=44.413***
Note: Dependent Variable: Purchase Intention. *P< .05, **P< .01, ***P< .001.
Table 4 shows that the gender of consumers did not moderate the correlation between (a) the
organic label and purchase intention, (b) the food safety and purchase intention, and (c) the green
product and purchase intention. As a result, Model Two failed to accept H7, H8, and H9.
Table 4: Comparing Two Estimates of Male and Female
Test of Interaction Male Female z value
B1 SE b1 B2 SE b2
Organic Label .324 .072 .353 .053 -.324
Food Safety .187 .068 .270 .051 -.976
Greed product .191 .067 .123 .056 .778
5. DISCUSSION AND CONCLUSION
5.1. Discussion
This paper only focused on consumers’ intention relative to purchasing green products and adopted
influential factors from consumers’ psychological elements in terms of the CSA features. Based
on Table 2, Table 3, Table 4, and Table AT2, the results speculated that this group of consumers,
who take care of health as lifestyles of health and sustainability, was because of their lifecycle.
Furthermore, if one wanted to rank the purchase intention by influenced factors in terms of Table
240 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
2: first was awareness of certified organic label, second was food safety attitude, third was green
product awareness.
Regarding the awareness of certified organic label, the result indicated the organic label directly
influenced the consumers’ purchase intention. Furthermore, the organic label had a strong
significant correlation with consumer’s food safety attitude and green product awareness,
according to Table 2 and Table AT2. The reason was that the Chinese community experienced
many food safety incidents from distinguished brands or companies, such as KFC and McDonald’s
since 2008 (Jourdan, 2014; Li, 2014). It may be the reason for Chinese consumers who believed in
the green certificate system and evaluation process, even though the evaluation system was
incapable to guarantee the foods was 100% safe.
Relating to the food safety attitude, as described by Yeung and Yee (2012), nowadays consumers
continuously take care of food safety when they select foods. Also, the finding of this paper
indicated that income had a significant influence on the correlation between food safety attitude
and purchase intention of consumers. Specifically, Table AT 2 shows that these old and high-
income consumers have a strong food safety attitude. When consumers have a high level of food
safety attitude, they have a high purchase intention too. Also, the research by Fisher et al. (2012)
indicated that older consumers have switched from non-green products to green products in the
US.
About the green product awareness, consumers were required to understand the concept of the
green product and have environmental knowledge. However, nowadays not all consumers realize
the definition of green products (Ottman, 2004), and how green products relate to environmental
friendly issues (Laheri & Anupam, 2015). This paper found that green product awareness had a
positive influence on the consumers’ purchase intention, which was similar to the research by
Yaacob and Zakaria (2011). Therefore, consumers should have the knowledge of green product
awareness and how to relate their purchase intention based on Table TA2.
On the subject of the consumers’ income, the results indicated that the income affected purchase
intention, as the cost of organic vegetables was premium, they had to pay in advance. Table AT2
presents that consumers with high income, who have a higher purchase intention than low-income
consumers. Either in green product purchasing or green marketing, the income was an important
factor which obviously did affect the purchase intention and behavior of green consumers in China
(Wong & Mo, 2013; Wong & Zeng, 2015). It is assumed that when consumers are worrying
whether their foods are safe or not, their income will be one of the major factors to affect their
purchasing capacity in China’s market.
For the consumers’ gender, this paper found that the gender did not moderate the relation between
organic label, food safety, green product, and consumers’ purchase intention. Even though the
result of the paper differed from Fisher et al. (2012), this main reason may be in connection with
the consumers’ living environment; the research by Fisher et al. was in the US while this paper
was in China. Specifically, a couple of young people in China has their own career, and thus, they
do not decide “who” will be the person in charge to purchase vegetables daily. Furthermore, Wong
and Mo (2013) also indicated that the gender of consumers did not affect their purchase intention,
which turns into purchase behavior for a recycled product in China.
Wong Ming Wong, Shian-Yang Tzeng 241
5.2. Conclusion
There are two research questions in the initial paragraphs. In response to the first research question
- who will buy organic vegetables, the answer is old and high-income consumers. In response to
the second research question - what factors will affect consumers’ purchase intention, the answer
is that (a) the awareness of certified organic label, (b) food safety attitude, and (c) green product
awareness have directly affected consumers’ intention to purchase original vegetables. In other
words, from the view of the CSA model, its potential consumers are old and high-income people,
either male or female, and have a high purchasing intention that is related to (a) awareness of
certified organic label, (b) food safety attitude, and (c) green product awareness.
This paper revealed two points. First, it is related to the ranking in order of the influenced factors
on consumers’ intention. The ranking in order was the organic label, food safety, and green product
based on Table 2. Therefore, firms should acquire a certified green label or logo for their organic
foods. At the same time, firms should open its information on how to grow and deliver their foods;
thus, the tracking number is one reasonable tool to communicate with farmers and consumers.
Second, regarding its market segmentation, firms should target who are old and high-income
consumers based on Table AT2. This group of consumers presumed that they take care more of
their healthy life and lifestyle as well as food safety. Based on this inference, firms should have a
communication channel to communicate with this group of consumers to share the concept of the
original vegetables. The content of the communication should have (a) how to educate consumers’
organic product knowledge to relate the healthy life and (b) how to build up trust between
consumers and firms.
This paper has explored how consumers’ psychographics affect their purchase intention via the
CSA model based on the Wheel of Consumer Analysis by Peter and Olson (2010). Since the paper
was related to organic food purchase behavior, added income of consumers provided an evidence
for green consumer studies. Except for these factors from consumers’ psychographics, specifically,
when consumers behavior was related to green product consumption, their income was an index to
measure consumers intention who were able to purchase green products or not. In other words, to
implicate any theory of green consumer behavior in the future, income should be adopted to explore
the green consumer behavior when consumers have food safety attitude.
This paper had two contributions for a business environment that specifically had related to the
CSA model. First, firms should develop the concept of green products to integrate with green
marketing in the marketplace. The objective is to let consumers understand what green products
are and how to protect the living environment through green products purchasing and consumption
behavior (Kotler & Keller, 2016; Ottman, 2004). Second, all green products were delivered by
tracking the number to consumers, specifically in the China market. This carries the means of
developing the consumers “know-how” in obtaining the green certificates, logos, and labels in
order to build consumers’ belief and trust (Hoyer & Maclnnis, 2010; Peter & Olson, 2010;
Solomon, 2015).
5.3. Limitation and Future Research
242 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
This paper has two limitations. First is the location of the research sample. The paper collected
research samples from three cities in Guangdong province, China because the CSA model of
suppliers was limited. Therefore, future research may collect research samples in other cities in
China or other countries such as Japan, Malaysia. Second, the paper only adopted three factors to
explore its relationship with consumers’ purchase intention that was moderated by either the
income or gender. This research framework only explains approximately 32% of the influential
factors on green consumers intention of the CSA model. Therefore, the further research model
should add other factors to explore, such as price, trust, environmental awareness, past purchasing
behavior, to have an accurate and reasonable research model.
REFERENCES
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50(2), 179-211.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior.
Englewood Cliffs, NJ: Prentice-Hall.
Armstrong, G., Kotler, P., & Opresnik, M. O. (2017). Marketing: An introduction (13th ed.). Upper
Saddle River, NJ: Pearson.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51(6), 1173-1182.
Borin, N., Cerf, D. C., & Krishnan, R. (2011). Consumer effects of environmental impact in
product labeling. Journal of Consumer Marketing, 28(1), 76-86.
Castka, P., & Corbett, C. J. (2016). Governance of eco-labels: Expert opinion and media coverage.
Journal of Business Ethics, 135(2), 309-326.
Chauhan, V. (2011, November 16). Green Marketing. Retrieved July 18, 2016, from
http://philipkotler2013.blogspot.my/2011/11/green-marketing.html
Ching, S., & Tin, S. (2017, September 2). Food Safety in China is a Global Issue Requiring
Stringent Action. Retrieved July 26, 2018, from
https://www.scmp.com/comment/letters/article/2109330/food-safety-china-global-issue-
requiring-stringent-action
Cohen, J., & Cohen, P. (1983). Applied Multiple Regression Correlation Analysis for the
Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Cucchiara, C., Kown, S., & Ha, S. (2015). Message framing and consumer responses to organic
seafood labeling. British Food Journal, 117(5), 1547-1563.
Dahlstrom, R. (2011). Green Marketing Management. Independence, KY: South-Western
Cengage.
FairShare CSA Coalition. (2016a). Intro &History. Retrieved June 6, 2016, from
http://www.csacoalition.org/about-csa/intro-and-history/
FairShare CSA Coalition. (2016b). How it Works/FAQS. Retrieved June 6, 2016, from
http://www.csacoalition.org/about-csa/how-it-works-faqs/
Feng, H., Feng, J., Tian, D., & Mu, W. (2012). Consumers’ perceptions of quality and safety for
grape products: A case study in Zhejiang Province, China. British Food Journal,
1114(11), 1587-1598.
Wong Ming Wong, Shian-Yang Tzeng 243
Fisher, C., Bashyal, S., & Bachman, B. (2012). Demographic impacts on environmentally friendly
purchase behaviors. Journal of Targeting, Measurement and Analysis for Marketing,
20(3/4), 172-184.
Fornell, C. D., & Larcker, F. (1981). Evaluating structural equation models with unobservable.
Journal of Marketing Research, 18(1), 39-50.
Guagnano, G. A., Stern, P. C., & Dietz, T. (1995). Influences on attitude-behavior relationships a
natural experiment with curbside recycling. Environment and Behavior, 27(5), 699-718.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data
Analysis (6th ed.). Upper Saddle River, NJ: Person.
Hatton, C. (2015, September 30). Will China's New Food Safety Rules Work? Retrieved July 18,
2016, from http://www.bbc.com/news/blogs-china-blog-34398412
Hitchman, J. (2015, June 9). Community Supported Agriculture Thriving in China. Retrieved July
20, 2018, from https://www.ileia.org/2015/06/09/community-supported-agriculture-
thriving-china/
Hoyer, W. D., & Maclnnis, D. J. (2010). Consumer Behavior (5th ed.). Mason, OH: South-
Western.
Hunt, K. (2015, January 16). Why Chinese Food Safety is so Bad. Retrieved July 26, 2018, from
https://www.cnn.com/2015/01/16/world/china-food-safety/index.html
Joshi, Y., & Rahman, Z. (2015). Factors affecting green purchase behaviour and future research
directions. International Strategic Management Review, 3, 128-143.
Jourdan, A. (2014, July 22). China Food Scandal Spreads, Drags in Starbucks, Burger King and
McNuggets in Japan. Retrieved July 18, 2016, from http://www.reuters.com/article/us-
china-food-idUSKBN0FR07K20140722
JustFood. (2016a). History. Retrieved June 6, 2016, from http://www.justfood.org/csa/history
JustFood. (2016b). Frequently Asked Questions about CSA. Retrieved June 6, 2016, from
http://www.justfood.org/csa/faq
Khare, A., Mukerjee, S., & Goyal, T. (2013). Social influence and green marketing: An exploratory
paper on Indian consumers. Journal of Customer Behavior, 12(4), 361-381.
Kotler, P., & Keller, K. L. (2016). Marketing Management (15th ed.). Upper Saddle River, NJ:
Pearson.
Laheri, V. K., & Anupam. (2015). Consumer behavior towards purchase of green products with
specific product category. Indian Journal of Management Science, 5(1), 66-73.
Langiano, E., Ferrara, M., Lanni, L., Viscardi, V., Abbatecola, A. M., & De Vito, E. (2012). Food
safety at home: Knowledge and practices of consumers. Journal of Public Health, 20(1),
47-57.
Li, Z. (2014, July 30). China’s Tainted Meat Scandal Explained. Retrieved July 17, 2016, from
http://edition.cnn.com/2014/07/29/world/asia/explainer-china-meat-scandal/
Local Harvest. (2016). Community Supported Agriculture: Find a local CSA. Retrieved June 7,
2016, from http://www.localharvest.org/csa/
Narine, L. K., Ganpat, W., & Seepersad, G. (2015). Demand for organic produce. Journal of
Agribusiness in Developing and Emerging Economies, 5(1), 76-91.
The New York Time. (2018). Food Safety in China. Retrieved July 26, 2018, from
https://www.nytimes.com/topic/destination/food-safety-in-china
Olander, F., & ThØgersen, J. (1985). Understanding of consumer behaviour as a prerequisite for
environmental protection. Journal of Consumer Policy, 18(4), 345-385.
244 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
Ottman, J. (2004). Green Marketing: Opportunity for Innovation (2nd ed.). New York, NY:
BookSurge.
Papadopoulos, I., Karagouni, G., Trigkas, M., & Beltsiou, Z. (2014). Mainstreaming green product
strategies. EuroMed Journal of Business, 9(3), 293-317.
Peter, J. P., & Olson, J. C. (2010). Consumer Behavior and Marketing Strategy (9th ed.). New
York, NY: McGraw-Hill/Irwin.
Phippls, M., Ozanne, L. K., Luchs, M. G., Subrahmanyan, K., Kapitan, S., Catlin, J. R., & Weaver,
T. (2013). Understanding the inherent complexity of sustainable consumption: A social
cognitive framework. Journal of Business Research, 66(8), 1227-1234.
Pride, W. M., & Ferrell, O. C. (2016). Marketing (18th ed.). Boston, MA: Cengage Learning.
Purohit, H. C. (2012). Product positioning and consumer attitude towards eco-friendly labeling.
Journal of Management Research, 12(3), 153-162.
Quah, S. H., & Tan, A. K. G. (2010). Consumer purchase decisions of organic food products: An
ethnic analysis. Journal of International Consumer Marketing, 22, 47-55.
Rahbar, E., & Wahid, N. A. (2011). Investigation of green marketing tools' effect on consumers'
purchase behavior. Business Strategy Series, 12(2), 73-83.
Ramzy, A., & Yang, L. (2008, September 16). Tainted-Baby-Milk scandal in China. Retrieved July
18, 2016, from http://content.time.com/time/world/article/0,8599,1841535,00.html
Smith, J. (2017, March 6). Understanding China City Tiers and Where to Target Your Export.
Retrieved July 26, 2018, from https://www.businesswest.co.uk/blog/understanding-
china-city-tiers-and-where-target-your-export
Solomon, M. R. (2015). Consumer Behavior: Buying, Having, and Being (11th ed.). Upper Saddle
River, NJ: Pearson.
Speer, M. (2011, December 9). What is a Green Product? Retrieved July 18, 2016, from
http://www.isustainableearth.com/green-products/what-is-a-green-product
Wong, W. M., & Mo, H. F. (2013). Purchase behavior related to recycled product in China. Review
in Business Research, 13(2), 125-131.
Wong, W. M., & Zeng, X. Y. (2015). Price and quality of remanufactured products related to
consumer behavior. International Journal Trade and Global Markets, 8(1), 17-27.
Yaacob, M. R., & Zakaria, A. (2011). Customers' awareness, perception and future prospects of
green products in Pahang, Malaysia. Journal of Commerce, 3(2), 1-10.
Yeung, R., & Yee, W. M. (2012). Food safety concern: Incorporating marketing strategies into
consumer risk coping framework. British Food Journal, 114(1), 40-53.
Wong Ming Wong, Shian-Yang Tzeng 245
APPENDIX
Table AT1: Construct and Scale Item of Questionnaire
Construct Scale Item
Awareness
of Organic
Label
1) Organic labeling can identify the differences in organic vegetables and non-organic
vegetables.
2) Organic labeling can guarantee the safety of vegetables.
3) Organic labeling can guarantee the quality of vegetables.
4) I am more likely to trust the certified organic vegetables.
5) I would prefer purchasing these organic vegetables with certified logo.
Food
Safety
Attitude
1) I often worry about the food safety and quality problems.
2) I worry about the current quality and safety of vegetables in the marketplace.
3) I understand that long-term consumption of excessive pesticide residues in vegetables
may cause chronic poisoning to the body.
Green
Product
Awareness
1) I have heard of organic vegetables.
2) I can identify organic vegetables by certified organic labeling or logo.
3) I understand the definition and production standards of organic vegetables.
4) I know the difference between organic vegetables and non-organic vegetables.
5) I believe that organic vegetables are safer than non-organic vegetables because of non-
pesticide residues and heavy metals.
Purchase
Intention
1) I would consider buying the organic labeling or logo certification marked on organic
vegetables.
2) I am willing to purchase this organic labeling or logo for organic vegetables.
3) For safety reasons, I am willing to buy vegetables with organic labeling or logo.
4) If the price of organic vegetables is only 30% higher than non-organic vegetables, I
would be willing to purchase.
5) If it is convenient to buy, I would like to buy organic vegetables.
246 Consumers’ Psychographics and Green Consumption Intention:
Community Supported Agriculture Business Model in China
Table AT2: Demographic Attributes of the Respondents
Characteristics N = 486 Percent (%)
City
Shenzhen & Guangzhou 231 47.5
Shantou 255 52.5
Gender
Male 203 41.8
Female 283 58.2
Age
25 and Below 231 47.5
26-30 86 17.7
31-35 55 11.3
36-40 34 7
41-45 32 6.6
46-50 27 5.6
51-55 14 2.9
Above 56 7 1.4
Education
High School 111 22.8
Diploma 91 18.7
Undergraduate 248 51
Postgraduate 36 7.4
Monthly Income (CNY)
Below 4,000 141 29
4,001-8,000 165 34
8,001-12,000 95 19.5
Above 12,001 85 17.5
Table AT3: Coefficient Correlation Among Organic Label, Food Safety, Green Product,
Purchase Intention, Age, Education, and Income OL FS GP PI Age Education Income
OL 1
FS .341** 1
GP .418** .338** 1
PI .487** .404** .372** 1
Age .009 .139** -.015 .104* 1
Education -.081 -.074 -.036 -.041 -.251** 1
Income .055 .132** .074 .157** .224** .227** 1
Note: OL= Organic Label, FS = Food Safety, GP = Green Product, PI = Purchase Intention.
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).